Data Solutions & Delivery Manager

Data Leadership Meets AI-Powered Innovation | Data Solutions & Delivery Manager Location: New York, USA About the Role The Data Solutions & Delivery Manager is responsible for leading the design, development, architecture, and delivery of enterprise data solutions, with a strong focus on Databricks and modern cloud data platforms. This role combines technical leadership, solution ownership, delivery execution, and cross-functional team leadership to build scalable data platforms, lakehouse architectures, analytics capabilities, AI-enabled data solutions, and business-driven data solutions aligned with organizational objectives. The ideal candidate should have strong experience with Databricks and modern data architectures, with the ability to translate complex business requirements into scalable technology solutions while operating in a platform-agnostic environment and selecting the best-fit technologies across cloud providers and data ecosystems. Experience with AI/ML, Generative AI, Agentic AI, or building AI agents and intelligent applications on Databricks is a strong plus. Responsibilities Key Responsibilities Data Solutions, Databricks & Platform Leadership Lead the architecture, design, and delivery of scalable enterprise data solutions with Databricks as a core data and AI platform. Design and implement modern lakehouse architectures, data platforms, data pipelines, data products, analytics solutions, and AI-enabled applications using Databricks. Drive best practices across Databricks, Delta Lake, Unity Catalog, Lakeflow, data engineering, data governance, security, data quality, and platform operations. Design scalable batch and streaming data architectures and data pipelines using modern Databricks capabilities. Establish architecture and engineering standards for data ingestion, transformation, orchestration, data modeling, governance, security, monitoring, and performance optimization. Evaluate and implement Databricks capabilities based on business and technical requirements while maintaining a platform-agnostic approach across Azure, AWS, and GCP. Lead modernization initiatives involving migration from legacy data platforms and architectures to Databricks-based lakehouse solutions. Partner with business, data, engineering, and technology stakeholders to define data platform strategies and translate business requirements into scalable Databricks solutions. Drive continuous improvement of data platform reliability, scalability, performance, cost optimization, and operational efficiency. Required Skills Technical Expertise Databricks: Strong experience with Databricks as an enterprise data and AI platform, including Lakehouse architecture, Delta Lake, Unity Catalog, Lakeflow, data engineering, orchestration, governance, security, data quality, performance optimization, and platform operations. Data Engineering: ETL/ELT pipelines, data modeling, data integration, batch and streaming processing, incremental processing, Change Data Feed (CDF), and scalable data architectures. Cloud Platforms: Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), with the ability to design cloud-native and platform-agnostic solutions. Data Architecture: Lakehouse architectures, enterprise data platforms, data products, data warehouses, data lakes, and modern cloud-native data architectures. Enterprise Data Solutions: Customer Data Platforms (CDP), Master Data Management (MDM), Identity Resolution, analytics platforms, and enterprise data integration. Data Governance & Security: Unity Catalog, access control, data governance, data lineage, data quality, security, privacy, and enterprise data management practices. AI & Machine Learning: Experience integrating AI/ML capabilities with enterprise data platforms is preferred. Agentic AI: Experience with AI agents, Agentic AI, Generative AI applications, or intelligent data applications built on Databricks is a strong plus. Development Practices: Polyglot engineering, Agile delivery, DevOps, CI/CD, automation, infrastructure-as-code, and cloud-native engineering practices. Qualifications Required Experience and Skills 10+ years of experience in data engineering, enterprise data solutions, cloud platforms, and technology delivery. Strong hands-on experience with Databricks and modern lakehouse architectures, with demonstrated experience designing and delivering enterprise-grade data solutions on the platform is a must. Proven experience leading complex data initiatives involving multiple teams, technologies, and stakeholders. Strong understanding of modern data architectures, cloud platforms, analytics solutions, and enterprise data management practices. Experience designing and delivering solutions across major cloud providers such as Azure, AWS, and GCP. Strong leadership, communication, stakeholder management, and problem-solving skills. Ability to combine technical expertise, business understanding, and delivery leadership to drive successful outcomes. Experience with AI/ML, Generative AI, Agentic AI, or building AI agents/intelligent applications on Databricks is a strong plus. Preferred Skills Experience with AI/ML, Generative AI, Agentic AI, or building AI agents and intelligent applications on Databricks. Office Requirement: Minimum 3 days per week working from an office is mandatory. The location may be a MediaMint or client office depending on project requirements. #J-18808-Ljbffr Media Mint